Energy, exergy, and economic analysis of solar still using coal cylinder fins: an experimental study.

The phenomenon of drinking water scarcity has turned into the foremost issue that the world's populace is facing today. The Algerian desert regions, including the El Oued region (southeastern Algeria), also suffer from drinking water shortages, despite the presence of huge quantities of underground salty water. Solar distillation is among the easy and cheap solutions to solve this problem because this method relies on renewable energy (solar energy) as a key factor in providing drinking water from saltwater. As solar energy is intermittent energy, energy storage is required for augmenting the yield. Coal cylinders are low cost and locally available materials that can be used as fins and energy storage materials. In this research, two solar stills such as conventional solar still with a black absorber (CSS-BA) and CSS with a black absorber and coal cylinders (CSS-BA&CC) were fabricated and tested. The CSS-BA&CC produced a potable water yield of about 4.16 kg per day while the CSS-BA could only produce 2.88 kg, which is 44.44% lower when compared to the CSS-BA&CC. The thermal efficiency was noted to be 22.04 % and the exergy efficiency is 1.07 % in the CSS-BA. Similarly, the thermal efficiency is 32.46% and exergy efficiency is 1.90% for the CSS-BA&CC. The experimental results proved that the potable water yield of the CSS-BA was enhanced by coal cylinders (sensible heat energy storage material -SHESM) which is a locally available low-cost material.

RGB-D ergonomic assessment system of adopted working postures.

Ensuring a healthier working environment is of utmost importance for companies and global health organizations. In manufacturing plants, the ergonomic assessment of adopted working postures is indispensable to avoid risk factors of work-related musculoskeletal disorders. This process receives high research interest and requires extracting plausible postural information as a preliminary step. This paper presents a semi-automated end-to-end ergonomic assessment system of adopted working postures. The proposed system analyzes the human posture holistically, does not rely on any attached markers, uses low cost depth technologies and leverages the state-of-the-art deep learning techniques. In particular, we train a deep convolutional neural network to analyze the articulated posture and predict body joint angles from a single depth image. The proposed method relies on learning from synthetic training images to allow simulating several physical tasks, different body shapes and rendering parameters and obtaining a highly generalizable model. The corresponding ground truth joint angles have been generated using a novel inverse kinematics modeling stage. We validated the proposed system in real environments and achieved a joint angle mean absolute error (MAE) of 3.19±1.57∘ and a rapid upper limb assessment (RULA) grand score prediction accuracy of 89% with Kappa index of 0.71 which means substantial agreement with reference scores. This work facilities evaluating several ergonomic assessment metrics as it provides direct access to necessary postural information overcoming the need for computationally expensive post-processing operations.